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Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data
IMPORTANCE: COVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). OBJECTIVE: We filled this gap by assessing relevant data availability and quality across states, and in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536886/ https://www.ncbi.nlm.nih.gov/pubmed/33024984 http://dx.doi.org/10.1101/2020.10.01.20205377 |
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author | Pathak, Ishaan Choi, Yoonjoung Jiao, Dazhi Yeung, Diana Liu, Li |
author_facet | Pathak, Ishaan Choi, Yoonjoung Jiao, Dazhi Yeung, Diana Liu, Li |
author_sort | Pathak, Ishaan |
collection | PubMed |
description | IMPORTANCE: COVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). OBJECTIVE: We filled this gap by assessing relevant data availability and quality across states, and in states with available data, investigating how racial-ethnic disparities in CFR changed after age adjustment. DESIGN/SETTING/PARTICIPANTS/EXPOSURE: We conducted a landscape analysis as of July 1st, 2020 and developed a grading system to assess COVID-19 case and death data by age and race in 50 states and DC. In states where age- and race-specific data were available, we applied direct age standardization to compare CFR across race-ethnicities. We developed an online dashboard to automatically and continuously update our results. MAIN OUTCOME AND MEASURE: Our main outcome was CFR (deaths per 100 confirmed cases). We examined CFR by age and race-ethnicities. RESULTS: We found substantial variations in disaggregating and reporting case and death data across states. Only three states, California, Illinois and Ohio, had sufficient age- and race-ethnicity-disaggregation to allow the investigation of racial-ethnic disparities in CFR while controlling for age. In total, we analyzed 391,991confirmed cases and 17,612 confirmed deaths. The crude CFRs varied from, e.g. 7.35% among Non-Hispanic (NH) White population to 1.39% among Hispanic population in Ohio. After age standardization, racial-ethnic differences in CFR narrowed, e.g. from 5.28% among NH White population to 3.79% among NH Asian population in Ohio, or an over one-fold difference. In addition, the ranking of race-ethnic-specific CFRs changed after age standardization. NH White population had the leading crude CFRs whereas NH Black and NH Asian population had the leading and second leading age-adjusted CFRs respectively in two of the three states. Hispanic population’s age-adjusted CFR were substantially higher than the crude. Sensitivity analysis did not change these results qualitatively. CONCLUSIONS AND RELEVANCE: The availability and quality of age- and race-ethnic-specific COVID-19 case and death data varied greatly across states. Age distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR. Age standardization narrows racial-ethnic disparities and changes ranking. Public COVID-19 data availability, quality, and harmonization need improvement to address racial disparities in this pandemic. |
format | Online Article Text |
id | pubmed-7536886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-75368862020-10-07 Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data Pathak, Ishaan Choi, Yoonjoung Jiao, Dazhi Yeung, Diana Liu, Li medRxiv Article IMPORTANCE: COVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). OBJECTIVE: We filled this gap by assessing relevant data availability and quality across states, and in states with available data, investigating how racial-ethnic disparities in CFR changed after age adjustment. DESIGN/SETTING/PARTICIPANTS/EXPOSURE: We conducted a landscape analysis as of July 1st, 2020 and developed a grading system to assess COVID-19 case and death data by age and race in 50 states and DC. In states where age- and race-specific data were available, we applied direct age standardization to compare CFR across race-ethnicities. We developed an online dashboard to automatically and continuously update our results. MAIN OUTCOME AND MEASURE: Our main outcome was CFR (deaths per 100 confirmed cases). We examined CFR by age and race-ethnicities. RESULTS: We found substantial variations in disaggregating and reporting case and death data across states. Only three states, California, Illinois and Ohio, had sufficient age- and race-ethnicity-disaggregation to allow the investigation of racial-ethnic disparities in CFR while controlling for age. In total, we analyzed 391,991confirmed cases and 17,612 confirmed deaths. The crude CFRs varied from, e.g. 7.35% among Non-Hispanic (NH) White population to 1.39% among Hispanic population in Ohio. After age standardization, racial-ethnic differences in CFR narrowed, e.g. from 5.28% among NH White population to 3.79% among NH Asian population in Ohio, or an over one-fold difference. In addition, the ranking of race-ethnic-specific CFRs changed after age standardization. NH White population had the leading crude CFRs whereas NH Black and NH Asian population had the leading and second leading age-adjusted CFRs respectively in two of the three states. Hispanic population’s age-adjusted CFR were substantially higher than the crude. Sensitivity analysis did not change these results qualitatively. CONCLUSIONS AND RELEVANCE: The availability and quality of age- and race-ethnic-specific COVID-19 case and death data varied greatly across states. Age distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR. Age standardization narrows racial-ethnic disparities and changes ranking. Public COVID-19 data availability, quality, and harmonization need improvement to address racial disparities in this pandemic. Cold Spring Harbor Laboratory 2020-10-04 /pmc/articles/PMC7536886/ /pubmed/33024984 http://dx.doi.org/10.1101/2020.10.01.20205377 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/It is made available under a CC-BY-NC-ND 4.0 International license (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Article Pathak, Ishaan Choi, Yoonjoung Jiao, Dazhi Yeung, Diana Liu, Li Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data |
title | Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data |
title_full | Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data |
title_fullStr | Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data |
title_full_unstemmed | Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data |
title_short | Racial-ethnic disparities in case fatality ratio narrowed after age standardization: A call for race-ethnicity-specific age distributions in State COVID-19 data |
title_sort | racial-ethnic disparities in case fatality ratio narrowed after age standardization: a call for race-ethnicity-specific age distributions in state covid-19 data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7536886/ https://www.ncbi.nlm.nih.gov/pubmed/33024984 http://dx.doi.org/10.1101/2020.10.01.20205377 |
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